Image capturing devices (CMOS, CCD sensor) are usually influenced by noises during image capturing, which results in random noise in the videos, and noises are even more serious especially at low illumination conditions. Therefore, it is necessary to remove noises by means of video denoising technologies. In addition, with the development of mobile internet and as videos are becoming more and more multi-sourced, various video sources comprising internet videos shot by handheld devices need to be displayed on display terminal devices such as a television. However, owing to the limited area of the sensors of cameras in the handheld mobile devices, the imaging quality of handheld mobile devices is not good and the noise is more serious as compared to large-area sensors of professional camera devices, so video denoising technologies become particularly important.
Video noise reduction technology includes spatial noise reduction and temporal noise reduction technologies, wherein the spatial noise reduction technology includes the simple spatial filtering, such as mean filtering and median filtering, which will usually result in blurring of details, while the temporal noise reduction technology can better protect details, so it is more widely used in the industry. A conventional temporal noise reduction method is as shown in FIG. 1, wherein an inter-frame difference is calculated from a current input frame and a previous filtered frame; the inter-frame difference is then compared with a threshold to perform motion detection, that is, pixels whose inter-frame difference is greater than a threshold are motion pixels, and pixels whose inter-frame difference is smaller than the threshold are still pixels; then the temporal filtering between the current input frame and the previous filtered frame is performed based on the result of motion detection. If it is a still region, multi-frame weighted temporal filtering is performed to achieve the effect of denoising, and if it is a motion region, then no temporal filtering is performed.
Generally, two types of error occur in motion detection. One type of error is missed detection, i.e. a moving pixel is determined as a still pixel, which will cause the multi-frame weighted temporal filtering to be performed on the motion region, resulting in tailing of a moving object or motion blurring. The other type of error is false alarm, i.e. a still pixel is erroneously identified as a moving pixel, which will cause that no temporal filtering is performed on the still region, thus noises in the still region cannot be removed. If the threshold for motion detection is high, the error of missed detection will easily occur; and if the threshold for motion detection is low, the error of false alarm will easily occur.
Conventional motion detection methods, such as the methods proposed in patents U.S. Pat. Nos. 7,903,179B2, 6,061,100 and US 2006/0139494A1, usually use a predefined global threshold or a noise level adaptive global threshold to perform motion detection. For example, in patent U.S. Pat. No. 6,061,100, a two times noise level is used as the threshold for motion detection, and if the inter-frame difference is less than the two times noise level, the pixel is a still pixel; otherwise, the pixel is a moving pixel. This motion detection method usually only considers the statistical distribution of the still pixel, and when the noise is white noise and Gaussian, it will ensure that more than 95% still pixels will not be detected as moving pixels, that is, the rate of occurrence of the second type of error, i.e. false alarm, is below 5%, but the rate of occurrence of the first type error, i.e. missed detection, cannot be controlled. For a motion video with low contrast (i.e. a video in which the difference between the brightness of the motion target and the brightness of the background is small), such a threshold selection method will result in a lot of missed detection, i.e. many motion regions are not detected, thus moving object tailing and motion blurring will occur during temporal filtering, which are more serious problems than un-removed noises in terms of subjective image quality.
Therefore, it is necessary to solve the problem concerning how to control the error of missed detection in the low-contrast region at the same time so that moving object tailing and motion blurring will not occur in the low contrast region.